pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling
We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matri...
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| Published in | Bioinformatics Vol. 29; no. 18; pp. 2363 - 2364 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
15.09.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 1460-2059 |
| DOI | 10.1093/bioinformatics/btt402 |
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| Abstract | We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.
Availability: The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.
Contact: victor.guallar@bsc.es
Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.
The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.
victor.guallar@bsc.es
Supplementary data are available at Bioinformatics online. We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.SUMMARYWe introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.AVAILABILITYThe source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.victor.guallar@bsc.esCONTACTvictor.guallar@bsc.esSupplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.Availability: The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.Contact: victor.guallarsc.esSupplementary information: Supplementary data are available at Bioinformatics online. We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms. Availability: The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section. Contact: victor.guallar@bsc.es Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Gil, Víctor A. Guallar, Víctor |
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| Title | pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling |
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